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Neural Ordinary Differential Equations
v1v2v3v4v5 (latest)

Neural Ordinary Differential Equations

19 June 2018
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Neural Ordinary Differential Equations"

50 / 3,220 papers shown
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev
A. Katrutsa
L. Markeeva
Julia Gusak
A. Cichocki
Ivan Oseledets
151
8
0
11 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering
  and Environmental Systems
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental SystemsACM Computing Surveys (ACM CSUR), 2020
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
684
540
0
10 Mar 2020
Lagrangian Neural Networks
Lagrangian Neural NetworksInternational Conference on Learning Representations (ICLR), 2020
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
540
513
0
10 Mar 2020
Variational Learning of Individual Survival Distributions
Variational Learning of Individual Survival DistributionsACM Conference on Health, Inference, and Learning (CHIL), 2020
Zidi Xiu
Chenyang Tao
Benjamin A. Goldstein
Ricardo Henao
OOD
184
17
0
09 Mar 2020
Progressive Growing of Neural ODEs
Progressive Growing of Neural ODEsInternational Conference on Learning Representations (ICLR), 2020
Hammad A. Ayyubi
Yi Yao
Ajay Divakaran
AI4TS
67
2
0
08 Mar 2020
Convergence of Q-value in case of Gaussian rewards
Convergence of Q-value in case of Gaussian rewards
Konatsu Miyamoto
Masaya Suzuki
Yuma Kigami
Kodai Satake
102
1
0
07 Mar 2020
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable
  Neural Network for Dynamic Physical Processes
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Gurpreet Singh
Soumyajit Gupta
Matt Lease
Clint Dawson
AI4TSAI4CE
98
2
0
05 Mar 2020
Methods to Recover Unknown Processes in Partial Differential Equations
  Using Data
Methods to Recover Unknown Processes in Partial Differential Equations Using DataJournal of Scientific Computing (J. Sci. Comput.), 2020
Zhen Chen
Kailiang Wu
D. Xiu
109
3
0
05 Mar 2020
Bayesian System ID: Optimal management of parameter, model, and
  measurement uncertainty
Bayesian System ID: Optimal management of parameter, model, and measurement uncertaintyNonlinear dynamics (Nonlinear Dyn.), 2020
Nicholas Galioto
Alex Gorodetsky
146
37
0
04 Mar 2020
Forecasting Sequential Data using Consistent Koopman Autoencoders
Forecasting Sequential Data using Consistent Koopman AutoencodersInternational Conference on Machine Learning (ICML), 2020
Omri Azencot
N. Benjamin Erichson
Vanessa Lin
Michael W. Mahoney
AI4TSAI4CE
501
186
0
04 Mar 2020
Gaussianization Flows
Gaussianization FlowsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Chenlin Meng
Yang Song
Jiaming Song
Stefano Ermon
153
35
0
04 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised
  Video Prediction
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video PredictionComputer Vision and Pattern Recognition (CVPR), 2020
Vincent Le Guen
Nicolas Thome
AI4CEPINN
395
357
0
03 Mar 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism
  Principled Robust Deep Neural Nets
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural NetsInternational Conference on Machine Learning, Optimization, and Data Science (MOD), 2020
Thu Dinh
Bao Wang
Andrea L. Bertozzi
Stanley J. Osher
AAML
134
19
0
02 Mar 2020
Differentiating through the Fréchet Mean
Differentiating through the Fréchet MeanInternational Conference on Machine Learning (ICML), 2020
Aaron Lou
Isay Katsman
Qingxuan Jiang
Serge J. Belongie
Ser-Nam Lim
Christopher De Sa
DRL
322
77
0
29 Feb 2020
Learning Multivariate Hawkes Processes at Scale
Learning Multivariate Hawkes Processes at Scale
Maximilian Nickel
Matt Le
169
17
0
28 Feb 2020
Woodbury Transformations for Deep Generative Flows
Woodbury Transformations for Deep Generative FlowsNeural Information Processing Systems (NeurIPS), 2020
You Lu
Bert Huang
203
18
0
27 Feb 2020
Differentiable Molecular Simulations for Control and Learning
Differentiable Molecular Simulations for Control and Learning
Wujie Wang
Simon Axelrod
Rafael Gómez-Bombarelli
AI4CE
352
51
0
27 Feb 2020
ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory
  Networks
ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks
Radu Grosu
64
1
0
26 Feb 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie
  Groups on Arbitrary Continuous Data
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous DataInternational Conference on Machine Learning (ICML), 2020
Marc Finzi
Samuel Stanton
Pavel Izmailov
A. Wilson
409
360
0
25 Feb 2020
Learning Queuing Networks by Recurrent Neural Networks
Learning Queuing Networks by Recurrent Neural NetworksInternational Conference on Performance Engineering (ICPE), 2020
G. Garbi
Emilio Incerto
M. Tribastone
92
18
0
25 Feb 2020
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsNeural Information Processing Systems (NeurIPS), 2020
Ruizhi Deng
B. Chang
Marcus A. Brubaker
Greg Mori
Andreas M. Lehrmann
307
58
0
24 Feb 2020
Alternating the Population and Control Neural Networks to Solve
  High-Dimensional Stochastic Mean-Field Games
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field GamesProceedings of the National Academy of Sciences of the United States of America (PNAS), 2020
A. Lin
Samy Wu Fung
Wuchen Li
L. Nurbekyan
Stanley J. Osher
305
92
0
24 Feb 2020
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by
  Enabling Input-Adaptive Inference
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive InferenceInternational Conference on Learning Representations (ICLR), 2020
Ting-Kuei Hu
Tianlong Chen
Haotao Wang
Zinan Lin
OODAAML3DH
351
88
0
24 Feb 2020
Stochasticity in Neural ODEs: An Empirical Study
Stochasticity in Neural ODEs: An Empirical StudyInternational Conference on Learning Representations (ICLR), 2020
V. Oganesyan
Alexandra Volokhova
Dmitry Vetrov
BDL
198
21
0
22 Feb 2020
VFlow: More Expressive Generative Flows with Variational Data
  Augmentation
VFlow: More Expressive Generative Flows with Variational Data AugmentationInternational Conference on Machine Learning (ICML), 2020
Jianfei Chen
Cheng Lu
Biqi Chenli
Jun Zhu
Tian Tian
DRL
199
62
0
22 Feb 2020
Stochastic Normalizing Flows
Stochastic Normalizing Flows
Liam Hodgkinson
Christopher van der Heide
Fred Roosta
Michael W. Mahoney
BDL
135
1
0
21 Feb 2020
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free'
  Dynamical Systems
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical SystemsInternational Conference on Machine Learning (ICML), 2020
Hans Kersting
N. Krämer
Martin Schiegg
Christian Daniel
Michael Tiemann
Philipp Hennig
202
22
0
21 Feb 2020
Stochastic Latent Residual Video Prediction
Stochastic Latent Residual Video PredictionInternational Conference on Machine Learning (ICML), 2020
Jean-Yves Franceschi
E. Delasalles
Mickaël Chen
Sylvain Lamprier
Patrick Gallinari
VGen
410
163
0
21 Feb 2020
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and
  Control into Deep Learning
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
PINNAI4CE
321
87
0
20 Feb 2020
DDPNOpt: Differential Dynamic Programming Neural Optimizer
DDPNOpt: Differential Dynamic Programming Neural OptimizerInternational Conference on Learning Representations (ICLR), 2020
Guan-Horng Liu
T. Chen
Evangelos A. Theodorou
280
7
0
20 Feb 2020
Dissecting Neural ODEs
Dissecting Neural ODEsNeural Information Processing Systems (NeurIPS), 2020
Stefano Massaroli
Michael Poli
Jinkyoo Park
Atsushi Yamashita
Hajime Asama
263
237
0
19 Feb 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows
  and Latent Variable Models
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Chin-Wei Huang
Laurent Dinh
Aaron Courville
DRL
306
93
0
17 Feb 2020
Learning Group Structure and Disentangled Representations of Dynamical
  Environments
Learning Group Structure and Disentangled Representations of Dynamical Environments
Robin Quessard
Thomas D. Barrett
W. Clements
DRL
173
22
0
17 Feb 2020
Stochastic Normalizing Flows
Stochastic Normalizing FlowsNeural Information Processing Systems (NeurIPS), 2020
Hao Wu
Jonas Köhler
Frank Noé
529
212
0
16 Feb 2020
Hypernetwork approach to generating point clouds
Hypernetwork approach to generating point cloudsInternational Conference on Machine Learning (ICML), 2020
Przemysław Spurek
Sebastian Winczowski
Jacek Tabor
M. Zamorski
Maciej Ziȩba
Tomasz Trzciñski
3DPC
201
35
0
10 Feb 2020
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular
  Dynamics
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular DynamicsInternational Conference on Machine Learning (ICML), 2020
Alexander Tong
Jessie Huang
Guy Wolf
David van Dijk
Smita Krishnaswamy
225
233
0
09 Feb 2020
Incorporating Symmetry into Deep Dynamics Models for Improved
  Generalization
Incorporating Symmetry into Deep Dynamics Models for Improved GeneralizationInternational Conference on Learning Representations (ICLR), 2020
Rui Wang
Robin Walters
Rose Yu
AI4CE
532
198
0
08 Feb 2020
Learning Implicit Generative Models with Theoretical Guarantees
Learning Implicit Generative Models with Theoretical Guarantees
Yuan Gao
Jian Huang
Yuling Jiao
Jin Liu
209
7
0
07 Feb 2020
How to train your neural ODE: the world of Jacobian and kinetic
  regularization
How to train your neural ODE: the world of Jacobian and kinetic regularizationInternational Conference on Machine Learning (ICML), 2020
Chris Finlay
J. Jacobsen
L. Nurbekyan
Adam M. Oberman
397
329
0
07 Feb 2020
Linearly Constrained Neural Networks
Linearly Constrained Neural Networks
J. Hendriks
Carl Jidling
A. Wills
Thomas B. Schon
504
38
0
05 Feb 2020
Automatic structured variational inference
Automatic structured variational inferenceInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Luca Ambrogioni
Kate Lin
Emily Fertig
Sharad Vikram
Max Hinne
Dave Moore
Marcel van Gerven
BDL
282
31
0
03 Feb 2020
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud
  Processing
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud ProcessingInternational Conference on Learning Representations (ICLR), 2020
Xingzhe He
Helen Lu Cao
Bo Zhu
3DPC
135
10
0
01 Feb 2020
PDE-based Group Equivariant Convolutional Neural Networks
PDE-based Group Equivariant Convolutional Neural NetworksJournal of Mathematical Imaging and Vision (JMIV), 2020
B. Smets
J. Portegies
Erik J. Bekkers
R. Duits
AI4CE
566
63
0
24 Jan 2020
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid
  Body Dynamics
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body DynamicsIEEE International Conference on Robotics and Automation (ICRA), 2020
David Millard
Eric Heiden
Shubham Agrawal
Gaurav Sukhatme
AI4CE
148
16
0
22 Jan 2020
Learning to Control PDEs with Differentiable Physics
Learning to Control PDEs with Differentiable PhysicsInternational Conference on Learning Representations (ICLR), 2020
Philipp Holl
V. Koltun
Nils Thuerey
AI4CEPINN
265
208
0
21 Jan 2020
Transfer Learning using Neural Ordinary Differential Equations
Transfer Learning using Neural Ordinary Differential Equations
S. Rajath
Sumukh Aithal K
Natarajan Subramanyam
89
1
0
21 Jan 2020
Invertible Generative Modeling using Linear Rational Splines
Invertible Generative Modeling using Linear Rational SplinesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
H. M. Dolatabadi
S. Erfani
C. Leckie
385
67
0
15 Jan 2020
NODIS: Neural Ordinary Differential Scene Understanding
NODIS: Neural Ordinary Differential Scene UnderstandingEuropean Conference on Computer Vision (ECCV), 2020
Cong Yuren
H. Ackermann
Wentong Liao
M. Yang
Bodo Rosenhahn
364
17
0
14 Jan 2020
Universal Differential Equations for Scientific Machine Learning
Universal Differential Equations for Scientific Machine Learning
Christopher Rackauckas
Yingbo Ma
Julius Martensen
Collin Warner
K. Zubov
R. Supekar
Dominic J. Skinner
Ali Ramadhan
Alan Edelman
AI4CE
450
709
0
13 Jan 2020
Intelligence, physics and information -- the tradeoff between accuracy
  and simplicity in machine learning
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
Tailin Wu
336
2
0
11 Jan 2020
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